Internet of things location spoofing detection

ABSTRACT

A network device receives primary location data, associated with a device connect to an Internet of Things (IoT) device service, generated by a first location determining source that is located in the device. The network device further receives secondary location data, associated with the device, generated by a second location determining source that is located external to the device. The network device performs location spoofing detection of the device based on the primary location data and the secondary location data.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. application Ser. No.16/556,950 filed Aug. 30, 2019, now U.S. Pat. No. 10,798,581, which is acontinuation of U.S. application Ser. No. 16/108,941 filed on Aug. 22,2018, now U.S. Pat. No. 10,440,579, both of which are entitled “Internetof Things Location Spoofing Detection” and the contents of which areincorporated by reference herein in their entirety.

BACKGROUND

The “Internet of Things” (IoT) is a network of physical devices (i.e.,“things”) designed for specific functions, unlike general computingdevices like desktop or laptop computers. IoT devices are embedded withelectronics and network connectivity that enable these devices tocollect, store and exchange data. The network connectivity may include,for example, Bluetooth™ connectivity, WI-FI connectivity, and/orcellular network connectivity. An IoT device may additionally havecomputational capability, with various installed software (e.g., apps),and may also include one or more types of sensors. An IoT device may be,via the network connectivity, controlled remotely across existingnetwork infrastructure. An IoT device may use the network connectivityto communicate with other IoT devices, or with certain nodes (e.g., aparticular server or computer) across the Internet.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates an overview of exemplary IoT device locationspoofing detection using primary IoT device location data and secondaryIoT device location data obtained from different location determiningsources;

FIG. 1B illustrates an overview of exemplary IoT device locationconfidence determination using primary device location data andsecondary device location data obtained from different locationdetermining sources;

FIG. 2A illustrates an exemplary network environment in which IoT devicelocation spoofing detection and device location confidence determinationmay be implemented;

FIG. 2B depicts a portion of the network environment of FIG. 2A,according to one exemplary implementation, in which some IoT devicesconnect to a wireless Local Area Network, and other IoT devices connectto a cellular network;

FIG. 3 depicts exemplary components of a device that may correspond tothe user devices, thingspace platform, IoT devices, and IoT devicedatabase of FIG. 2A;

FIG. 4 depicts an exemplary device data structure that may be stored inthe IoT device database of FIG. 2A;

FIG. 5 depicts an exemplary configuration data structure that may bestored in the IoT device database of FIG. 2A;

FIG. 6 is a flow diagram that illustrates an exemplary process forsubscribing one or more IoT devices to an IoT network service, andproviding configuration data for configuring location spoofing detectionas applied to the subscribed one or more IoT devices;

FIGS. 7 and 8 depict exemplary user interfaces implemented on a userdevice for entering the subscription data and service configuration dataof the exemplary process of FIG. 6;

FIG. 9 is an exemplary messaging diagram associated with the process ofFIG. 6;

FIG. 10 is a flow diagram that illustrates an exemplary process forperforming location spoofing detection for an IoT device based oncurrent primary and secondary locations associated with the IoT device;

FIG. 11 is an exemplary messaging diagram associated with the process ofFIG. 10;

FIG. 12 is a flow diagram that illustrates an exemplary process fordetermining a confidence level associated with a location of an IoTdevice based on current primary and secondary locations associated withthe IoT device; and

FIG. 13 is an exemplary messaging diagram associated with the process ofFIG. 12.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following detailed description refers to the accompanying drawings.The same reference numbers in different drawings may identify the sameor similar elements.

The introduction of Long Term Evolution (LTE) Cat-M1 cellular networkservice enables low power wireless device connectivity to the cellularnetwork, such as, for example, for battery powered IoT,Machine-to-Machine (M2M), or other types of User Equipment (UE) devices.The LTE Cat-M1 network service supports a coarse device locationdetermining service based on Cat-M1 signaling with the IoT, M2M, or UEdevices. The coarse device location determining service provided by thecellular network identifies the cell (i.e., cell tower) to which aparticular IoT or M2M device is connected, or triangulates theparticular IoT (e.g., by multiple cell towers) to obtain a roughestimate of the IoT device geo-location.

For more accurate location information, IoT, M2M, or UE devicesfrequently incorporate built-in Global Positioning System (GPS) hardwarethat relies on GPS satellite signals to determine an accurategeo-location of the IoT or M2M devices. The IoT, M2M, or UE devices mayuse one of these different location determining services for obtaining acurrent location of the devices. In devices that use GPS for determiningan accurate location of the devices, however, false satellite signalsmay be sent to the IoT, M2M, or UE devices to cause the devices to be“spoofed” into reporting false locations.

Exemplary embodiments described herein utilize at least two differentsources of location data for a given IoT device to detect locationspoofing of the IoT device, and/or to determine a confidence level thatindicates a likelihood that the IoT device is actually located at thegeo-location indicated by the location data received from the source(s)of location data. For example, a primary location data source may be aGPS device residing in the IoT device that determines an accurategeo-location using GPS satellite signals, and the secondary locationdata source may be a wireless Local Area Network that determines alocation of a “hotspot” from which the IoT is transmitting. As anotherexample, the primary location data source may be the GPS device and GPSsatellite signals, and the secondary location data may be a cellularnetwork that determines a location of a cell tower with which the IoTdevice is communicating, or determines a geo-location of the IoT deviceby multiple cell towers triangulating signals from the IoT device. Aplatform, as described herein, uses the IoT device location data fromboth the primary and secondary location sources to detect locationspoofing of the IoT device and/or to assign a confidence level thatindicates likelihood that the IoT device is located at the primarylocation.

FIG. 1A illustrates an exemplary overview of IoT device locationspoofing detection using a primary IoT device location and a secondaryIoT device location obtained from two different IoT location determiningsources. As shown, an IoT device 100 may include a geo-location device,such as a Global Positioning System (GPS) device 105, that obtains acurrent geo-location of IoT device 100 as a primary IoT device location.IoT device 100 generates a location report 110, inserts the primary IoTdevice location in the report 110, and sends the report 110 to aplatform (not shown) within a thingspace cloud 115. The platform withinthe thingspace cloud 115 implements a spoofing detection function 120that receives the report 110 from IoT device 100. In one implementation,the location report 110 may be sent using a low-power and/or reduceddata rate cellular data connection typically reserved for IoT and M2Mdevices (e.g., Long Term Evolution (LTE) Category M1 (CAT-M1)). In otherimplementations, other types of wired or wireless connections may beused for sending location report 110 to spoofing detection function 120.

Additionally, a wireless location function 125, implemented within awireless network 130, determines a secondary location of IoT device 100based on wireless communications between IoT device 100 and one or morewireless access points 135, and sends the wireless-network basedlocation information 140 to spoofing detection function 120 inthingspace cloud 115. The wireless location function 125 determines thesecondary location of the IoT device 100 using a different locationdetermining mechanism, possibly having a different level of accuracy,then that used to determine the primary IoT device location.

In one implementation, the wireless network 130 may include a PublicLand Mobile Network (PLMN), and the wireless access points 135 mayinclude one or more cell towers. In this cellular networkimplementation, the wireless location determining function 125 usessignaling between IoT device 100 and at least one cell tower todetermine the geo-location of IoT device 100. For example, the wirelesslocation determining function 125 may use signaling between IoT device100 and a connected cell tower, or multiple cell towers (e.g., threecell towers), to triangulate the geo-location of IoT device 100. Inanother implementation, the wireless network 130 may include a wirelesslocal area network (WLAN), such as, for example, a WI-FI network (e.g.,wireless LAN employing the IEEE 802.11 standard), and the wirelessaccess points 135 may include one or more WLAN wireless access points.In this WLAN implementation, the wireless location determining function125 uses signaling between IoT device 100 and the one or more WLANwireless access points to determine which WLAN “hotspot” that IoT device100 is communicating with, to determine the known geo-location of that“hotspot,” and to determine if, and when, the IoT device 100 crossesfrom that “hotspot” to an adjacent “hotspot.” By tracking movement ofthe IoT device 100 between WLAN “hotspots,” the wireless locationdetermining function 125 can determine a “coarse” geo-location of IoTdevice 100.

Upon receipt of the primary IoT device location information/data (e.g.,from report 110), and the secondary IoT device location information/data(e.g., from wireless location determining function 125), spoofingdetection function 120 compares the primary location information withthe secondary location information to determine if the absolute value ofthe distance between them is greater than a certain threshold. If theabsolute value is greater than the threshold, spoofing detectionfunction 120 may indicate that the primary location information iserroneous and spoofed. Further details of exemplary implementations oflocation spoofing detection are described below with respect to FIG. 10.Upon detection of location spoofing, spoofing detection function 120sends a spoofing detection notification 145 to a user/subscriber 150,that may be an owner, operator, or administrator of the IoT device 100,or a user application executed by a user device (not shown) associatedwith the owner, operator, or administrator of the IoT device 100.

FIG. 1B illustrates an exemplary overview of IoT device locationconfidence determination using a primary IoT device location and asecondary IoT device location obtained from different IoT devicelocation determining sources. Similar to FIG. 1A, the IoT device 100 mayinclude a geo-location device, such as a Global Positioning System (GPS)device 105, that obtains a current geo-location of IoT device 100 as aprimary IoT device location. IoT device 100 generates a location report110, inserts the primary IoT device location in the report 110, andsends the report 110 to a platform (not shown) within a thingspace cloud115. The platform within the thingspace cloud 115 implements an IoTdevice location confidence determination function 155 that receives thereport 110 from IoT device 100. In one implementation, the locationreport 110 may be sent using a low-power and/or reduced data ratecellular data connection typically reserved for IoT and M2M devices(e.g., Long Term Evolution (LTE) Category M1 (CAT-M1)). In otherimplementations, other types of wired or wireless connections may beused for sending location report 110 to spoofing detection function 120.Additionally, a wireless location function 125, implemented within awireless network 130, determines a secondary location of IoT device 100based on wireless communications between IoT device 100 and one or morewireless access points 135, and sends the wireless-network basedlocation information 140 to IoT device location confidence determinationfunction 155 in thingspace cloud 115. The wireless location function 125determines the secondary location of the IoT device 100 using adifferent location determining mechanism, possibly having a differentlevel of accuracy, then that used to determine the primary IoT devicelocation.

In one implementation, the wireless network 130 may include a PLMN, andthe wireless access points 135 may include one or more cell towers. Inthis cellular network implementation, the wireless location determiningfunction 125 uses signaling between IoT device 100 and at least one celltower to determine the geo-location of IoT device 100. For example, thewireless location determining function 125 may use signaling between IoTdevice 100 and a connected cell tower, or multiple cell towers (e.g.,three cell towers), to triangulate the geo-location of IoT device 100.In another implementation, the wireless network 130 may include a WLAN,such as, for example, a WI-FI network, and the wireless access points135 may include one or more WLAN wireless access points. In this WLANimplementation, the wireless location determining function 125 usessignaling between IoT device 100 and the one or more WLAN wirelessaccess points to determine which WLAN “hotspot” that IoT device 100 iscommunicating with, determine the known geo-location of that “hotspot,”and determine if, and when, the IoT device 100 crosses from that“hotspot” to an adjacent “hotspot.” By tracking movement of the IoTdevice 100 between WLAN “hotspots,” the wireless location determiningfunction 125 can determine a “coarse” geo-location of IoT device 100.

Upon receipt of the primary IoT device location information/data (e.g.,from report 110), and the secondary IoT device location information/data(e.g., from wireless location determining function 125), IoT devicelocation confidence determination function 155 determines a confidencelevel associated with the IoT device 100 being located at the locationindicated by the primary location data. The confidence level may includeone of multiple different confidence levels that indicate a likelihoodthat the IoT device is located at the location indicated by the primarylocation data. In one implementation, the multiple different confidencelevels include “no confidence,” “low confidence,” and “high confidence”levels, described in more detail below. IoT device location confidencedetermination function 155, upon determining a confidence levelassociated with the IoT device 100 being located at the locationindicated by the primary location data, sends a notification 160 to auser/subscriber 150, that may be an owner, operator, or administrator ofthe IoT device 100, or a user application executed by a user device (notshown) associated with the owner, operator, or administrator of the IoTdevice 100. The notification 160 includes the determined IoT devicelocation confidence level, and other information, such as, for example,an identification of the IoT device 100.

FIG. 2A illustrates an exemplary network environment 200 in which IoTdevice location spoofing detection and device location confidencedetermination may be implemented. Network environment 200 may includeIoT devices 100-1 through 100-n, a thingspace platform 210, user devices220-1 through 220-m, an IoT device database (DB) 230, and a network(s)240.

-   -   Each of IoT devices 100-1 through 100-n (referred to herein as        “IoT device 100” or “IoT devices 100”) includes a physical        object or device (i.e., a “thing” or “M2M” device) that may be        designed for a specific function and which may be embedded with        electronics, memory storage, and network connectivity that        enables these objects or devices to collect, store and exchange        data with other IoT devices, with certain network nodes, or with        other devices via a PLMN of network(s) 240. Each IoT device        100's network connectivity may include, for example, Bluetooth™        connectivity, WLAN connectivity (e.g., WI-FI), and/or cellular        network connectivity.

Thingspace platform 210 includes one or more network devices that enablea user device 220 to register and enroll the one or more of IoT devices100-1 through 100-n for receiving a network service, such as, forexample, a CAT-M1 IoT device communication service, via a PLMN ofnetwork(s) 240. Thingspace platform 210 may reside in thingspace cloud115, and thingspace cloud 115 may connect to network(s) 240, or may be aportion of network(s) 240. Thingspace platform 210 may additionallyimplement spoofing detection function 120 and/or device locationconfidence determination function 155, already briefly described withrespect to FIGS. 1A and 1B.

Each of user devices 220-1 through 220-m (referred to herein as “userdevice 220” or “user devices 220”) includes an electronic device thatfurther includes a communication interface (e.g., wired or wireless) forcommunicating via network(s) 240. User device 220 may include, forexample, a cellular radiotelephone; a smart phone; a personal digitalassistant (PDA); a wearable computer; a desktop, laptop, palmtop ortablet computer; or a media player. User device 220 may, however,include any type of electronic device that includes a communicationinterface for communicating via network(s) 240. A user 150 (not shown inFIG. 2A) may be associated with each user device 220, where the user 150may be an owner, operator, administrator, and/or a permanent ortemporary user of user device 220, or application software executed byuser device 220. User 150 may additionally be an owner, operator, oradministrator of one or more IoT devices 100.

IoT device DB 230 includes one or more network devices that stores datastructures that enable the storage and retrieval of IoT devicesubscription enrollment data, and IoT location spoofing detectionconfiguration data.

Network(s) 240 may include one or more networks of various typesincluding, for example, a cellular public land mobile network (PLMN)(e.g., a Code Division Multiple Access (CDMA) PLMN, a Global System forMobile Communications (GSM) PLMN, a Long Term Evolution (LTE) PLMNand/or other types of PLMNs), a satellite mobile network, atelecommunications network (e.g., Public Switched Telephone Networks(PSTNs)), a wired and/or wireless LAN, a wired and/or wireless wide areanetwork (WAN), a metropolitan area network (MAN), an intranet, theInternet, or a cable network (e.g., an optical cable network). In oneimplementation, network(s) 240 may include a PLMN(s) or a satellitemobile network(s) connected to the Internet.

The configuration of the components of network environment 200 depictedin FIG. 2A is for illustrative purposes only, and other configurationsmay be implemented. Therefore, network environment 200 may includeadditional, fewer and/or different components, that may be configureddifferently, then depicted in FIG. 2A. For example, though a singlespoofing detection function 120 and device location confidencedetermination function 155 are depicted in FIG. 2A, thingspace platform210 may include multiple spoofing detection functions 120 and/or devicelocation confidence determination functions 155 implemented by multiple,different network devices in thingspace cloud 115 (e.g., for redundancy,or load balancing).

FIG. 2B depicts a portion of the network environment 200 of FIG. 2A,according to one exemplary implementation, in which some IoT devices 100connect to a WLAN, and other IoT devices 100 connect to a cellularnetwork. As shown, the networks 240 of FIG. 2A may include thingspacecloud 115, a WLAN(s) 130-1, and a cellular network(s) 130-2. LANwireless network(s) 130-1 may include one or more wireless LANs, suchas, for example, one or more WI-FI networks or WI-FI “hotspots.”Cellular network(s) 130-2 may include one or more cellular PLMNs orsatellite mobile networks. WLAN(s) 130-1 and cellular network(s) 130-2may connect to thingspace cloud 115 via wired or wireless links. IoTdevices 100-1 through 100-x (where x may be less than n) may wirelesslyconnect to LAN wireless network(s) 130-1, and IoT devices 100−x+1through 100-n may wirelessly connect to cellular network(s) 130-2.

A wireless location determining function 125-1 residing within WLAN(s)130-1 may, based on wireless communications between IoT devices 100-1through 100-x and LAN wireless network(s) 130-1 (and as described infurther detail below), determine a geo-location of each of the IoTdevices 100-1 through 100-x and provide the determined geo-locations tospoofing detection function 120 implemented by thingspace platform 210in thingspace cloud 115. Thingspace platform 210 may additionally storethe provided geo-locations in IoT device DB 230 in thingspace cloud 115.

A wireless location determining function 125-2 in cellular network(s)130-2 may, based on wireless communications between IoT devices 100-x+1through 100-n and cellular network(s) 130-2 (and as described in furtherdetail below), determine a geo-location of each of the IoT devices100-x+1 through 100-n and provide the determined geo-locations to thespoofing detection function 120 implemented by thingspace platform 210.Thingspace platform 210 may additionally store the providedgeo-locations in IoT device DB 230.

FIG. 3 depicts exemplary components of a device 300. User devices 220,thingspace platform 210, IoT devices 100, and IoT device DB 230 may eachinclude at least one device configured similarly to device 300, possiblywith some variations in components and/or configuration. Device 300 mayinclude a bus 310, a processing unit 320, a geo-location unit 325, amain memory 330, a read only memory (ROM) 340, a storage device 350, aninput device(s) 360, an output device(s) 370, and a communicationinterface(s) 380.

Bus 310 may include a path that permits communication among thecomponents of device 300. Processing unit 320 may include one or moreprocessors or microprocessors, or processing logic, which may interpretand execute instructions. Geo-location unit 325 may include a device fordetermining a geo-location of device 300. In one implementation,geo-location unit 325 may include a GPS device that determines a precisegeo-location of device 300 based on signals from a GPS satellite system.Main memory 330 may include a random access memory (RAM) or another typeof dynamic storage device that may store information and instructionsfor execution by processing unit 320. ROM 340 may include a ROM deviceor another type of static storage device that may store staticinformation and instructions for use by processing unit 320. Storagedevice 350 may include a magnetic and/or optical recording medium. Mainmemory 330, ROM 340 and storage device 350 may each be referred toherein as a “tangible non-transitory computer-readable medium” or a“non-transitory storage medium.”

Input device 360 may include one or more mechanisms that permit anoperator to input information to device 300, such as, for example, akeypad or a keyboard, a display with a touch sensitive panel, voicerecognition and/or biometric mechanisms, etc. Output device 370 mayinclude one or more mechanisms that output information to the device'soperator or user, including a display, a speaker, etc. Input device 360and output device 370 may, in some implementations, be implemented as agraphical user interface (GUI) that displays GUI information and whichreceives user input via the GUI. In an instance where device 300 is auser device 220, the GUI of input device 360 and output device 370 mayinclude a touch screen GUI that uses any type of touch screen device.Communication interface(s) 380 may include a transceiver that enablesdevice 300 to communicate with other devices and/or systems. Forexample, communication interface(s) 380 may include wired and/orwireless transceivers for communicating via network(s) 240. In aninstance where device 300 is an IoT device 100, communicationinterface(s) 380 may include a wireless transceiver for communicatingvia a WLAN 130-1 and/or a cellular network 130-2.

The configuration of components of device 300 shown in FIG. 3 is forillustrative purposes. Other configurations may be implemented.Therefore, device 300 may include additional, fewer and/or differentcomponents, arranged in a different configuration, then depicted in FIG.3. For example, an IoT device 100 may include similar components tothose shown in FIG. 3, but may omit input device(s) 360, outputdevice(s) 370, and storage device 350. As another example, thingspaceplatform 210 may also include similar components to those shown in FIG.3, but may omit geo-location unit 325.

FIG. 4 depicts an exemplary device data structure 400 that may be storedin IoT device DB 230. As shown, device data structure 400 may includemultiple records/entries 405 and each record/entry 405 may include asubscriber ID field 410, a device group ID field 415, a device type IDfield 420, and a device ID field 425.

Subscriber ID field 410 stores a unique identifier associated with aparticular user/subscriber 150 (i.e., that may be an owner, operator,and/or administrator of an IoT device 100, or a group of IoT devices100). The unique subscriber identifier may, for example, include themobile telephone number, email address, Medium Access Control (MAC)address, International Mobile Subscriber Identity (IMSI), orInternational Mobile Equipment Identity (IMEI) associated with theparticular user, or associated with a particular device of the user.Other types of unique subscriber identifiers may be used.

Device group ID field 415 stores a unique identifier associated with agroup of IoT devices 100 that may be owned, operated, and/oradministered by a same user/subscriber. The device group ID serves tocollectively identify the IoT devices 100 contained within a same group.

Device type ID field 420 stores an identifier that indicates a type ofIoT device of the IoT device(s) identified in field 415 or field 425.The type of IoT device may indicate a function the IoT device 100performs, a type of sensor data that the IoT device 100 generates, etc.

Device ID(s) field 425 stores a unique identifier associated with aparticular IoT device 100 within the group of IoT devices 100 identifiedby field 415. The unique device ID enables that particular IoT device100 to be distinguished from every other IoT device 100.

Device data structure 400 may be queried with, for example, a subscriberID to locate a record/entry 405 having a matching subscriber ID storedin field 410. When such a record/entry 405 is located, data may bestored in one or more of fields 410, 415, 420, and/or 425 of therecord/entry 405, or data may be retrieved from one or more of fields410, 415, 420, and/or 425 of the entry 405. Other fields of arecord/entry 405, instead of subscriber ID field 410, may be used forquerying device data structure 400, such as, for example, device groupID field 415, or device ID field 425.

FIG. 5 depicts an exemplary configuration data structure 500 that may bestored in IoT device DB 230. As shown, the configuration data structure500 may include multiple records/entries 505 and each record/entry 505may include a device group ID field 510, a location services (SVCs)supported field 515, a measurement accuracy field 520, a data sourcefield 525, a spoofing detection frequency field 530, a detectionnotification frequency field 535, a notification method field 540, and aspoofing detection enhancements field 545.

Device group ID field 510 stores a unique identifier associated with agroup of IoT devices 100 (e.g., that may be owned, operated, and/oradministered by a same user/subscriber). Location SVCs supported field515 stores data that indicates the particular location servicessupported by each device 100 of the group of devices identified in field510, or supported by the wireless network to which each device 100 ofthe group of devices identified in field 510 connects. For example,field 515 may store data that indicates that the devices of the groupidentified in field 510 each supports GPS geo-location determination,are connected to a WLAN that supports geo-location determination foreach IoT device, and/or are connected to a cellular network thatsupports geo-location determination for each IoT device.

Measurement accuracy field 520 stores data that indicates measurementaccuracies of the location determination services identified in field515. Data source field 525 stores data identifying a network address(es)of the device(s)/node(s) that supplies/supply the location data tothingspace platform 210. Spoof detection frequency field 530 stores datathat indicates a frequency at which the spoofing detection function 120of thingspace platform 210 performs spoofing detection for each of theIoT devices 100 of the group identified in field 510. Detectionnotification frequency field 535 stores data that indicates a frequencythat the user/subscriber associated with the group of IoT devices 100identified in field 510 is notified while spoofing is detected for anIoT device 100. Notification method field 540 stores data that indicatesa selected method of notifying the user/subscriber associated with thegroup of IoT devices 100, identified by field 510, of an occurrence of aspoofing detection for a IoT device 100. The method of notifying theuser/subscriber may include, for example, a text message, an email, astreaming notification, a target client notification delivery, or apolling notification delivery. Spoofing detection enhancements field 545stores data that indicates optional spoofing detection enhancements,that may be selected by the user/subscriber of the IoT devices in thegroup identified by field 510, for increasing the accuracy of thelocation spoofing detection for the IoT device(s).

Configuration data structure 500 may be queried with, for example, adevice Group ID to locate a record/entry 505 having a matching devicegroup ID stored in field 510. When such a record/entry 505 is located,data may be stored in one or more fields 510, 515, 520, 525, 530, 540,and/or 545 of the record/entry 505, or data may be retrieved from one ormore fields 510, 515, 520, 525, 530, 540, and/or 545 of the record/entry505. Other fields of a record/entry 505, instead of device group IDfield 510, may be used for querying data structure 500, such as, forexample, notification method field 540 or location services supportedfield 515.

Device data structure 400 and configuration data structure 500 aredepicted in FIGS. 4 and 5 as including tabular data structures withcertain numbers of fields having certain content. The tabular datastructures of data structures 400 and 500 shown in FIGS. 4 and 5,however, are for illustrative purposes. Other types of data structuresmay alternatively be used. The number, types, and content of the entriesand/or fields in the data structures 400 and 500 illustrated in FIGS. 4and 5 are also for illustrative purposes. Other data structures havingdifferent numbers of, types of and/or content of, the entries and/or thefields may be implemented. Therefore, data structures 400 and 500 mayinclude additional, fewer and/or different entries and/or fields thanthose depicted in FIGS. 4 and 5.

FIG. 6 is a flow diagram that illustrates an exemplary process forsubscribing one or more IoT devices 100 to an IoT network service, andproviding configuration data for configuring location spoofing detection(or other IoT network services) as applied to the subscribed one or moreIoT devices 100. The process of FIG. 6 may be implemented by thingspaceplatform 210, in conjunction with IoT device DB 230. The exemplaryprocess of FIG. 6 may be repeated for each IoT device 100, or each groupof IoT devices 100, to be subscribed to the IoT network service (e.g., aCATt-M1 network service) by a user/subscriber associated with the IoTdevice(s) 100. The process of FIG. 6 is described with respect to theexemplary user interfaces depicted in FIGS. 7 and 8, and the exemplarymessaging depicted in FIG. 9.

The process of FIG. 6 includes thingspace platform 210 receiving IoTdevice subscription data for an IoT device, or a group of IoT devices,including a subscriber ID, a device group ID, a device type ID, and adevice ID(s) (block 600). FIG. 7 depicts a first exemplary userinterface 700, implemented by a user device 220, for a user 150 tosupply the IoT device subscription data to thingspace platform 210. Asshown, user interface 700 includes a subscriber ID entry block 705, adevice group ID entry block 710, a device type ID entry block 715, andmultiple device ID entry blocks 720 (two are shown). The user 150 usesan input device 360 (e.g., a touch screen display) of user device 200 toenter a unique subscriber ID assigned to the user 150 in subscriber IDentry block 705; a unique device group ID assigned to the one or moreIoT devices 100 in the device group ID entry block 710, a device type IDassociated with the one or more IoT devices 100 in the device type IDentry block 715, and unique IDs for each IoT in the group of IoT devicesin respective device ID entry blocks 720. Upon completion of entry ofthe IoT device subscription data, the user 150 may select the userinterface button 730 (e.g., touch a touch screen display over adisplayed button) to submit the subscription enrollment data and tobegin configuring the location spoofing detection service, as describedwith reference to blocks 620-640 below. Alternatively, instead of userentry via user interface 700, the IoT device subscription data for theIoT device, or group of IoT devices, may be listed in a file (e.g., in a.csv file format) that may be uploaded to thingspace platform 210. FIG.9 depicts user device 220 supplying IoT device subscription data 900 tothingspace platform.

Thingspace platform 210 stores the IoT device subscription enrollmentdata in IoT device DB 230 (block 610). For each IoT device in the groupof IoT devices being enrolled in the IoT network service subscription,thingspace platform 210 stores the subscriber ID in field 410, thedevice group ID in field 415, the device type ID in field 420, and thedevice ID in field 425 of a record/entry 405 of device data structure400 stored in IoT device DB 230. Therefore, each IoT device 100 enrolledin the IoT network service subscription may have its own record/entry405 in device data structure 400. FIG. 9 depicts user device 220thingspace platform 210 storing 905 the IoT device subscription data inIoT device DB 230.

Thingspace platform 210 receives configuration data for configuring thelocation spoofing detection service for the device/device group (block620). FIG. 8 depicts a second exemplary user interface 800, implementedby, for example, a user device 220, for a user 105 to supplyconfiguration data to thingspace platform 210. As shown, user interface800 includes a location services supported entry region 805, a spoofdetection frequency region 815, a spoof detection notification frequencyregion 820, a notification method region 825, and a location spoofingdetection enhancements region 830.

Location services supported entry region 805 identifies the locationdetermining services that are provided by the IoT device 100 itself,and/or by the networks (e.g., WLAN 130-1 and/or cellular network 125-2)to which the IoT device 100 is connected. As shown, entry region 805 mayinclude a GPS checkbox, a cellular location services checkbox, or aWI-FI location services checkbox. The user 150 (i.e., owner, operator,and/or administrator) associated with a group of IoT devices may select(e.g., by touching each checkbox upon a touch screen display of a userdevice 220) the two or more location determining services that areavailable to the IoT devices within the group of IoT devices. In otherimplementations, thingspace platform 210 may determine the availablelocation determining services and automatically check the appropriatecheckboxes in entry region 805 of user interface 800. As further shownin FIG. 8, entry region 805 may include a measurement accuracy region810, that may be selectable for each location service having a checkedcheckbox in region 805, for viewing and/or entering a measurementaccuracy of the location determining service and a data source for thelocation determining service.

For example, in the example shown in FIG. 8, a group of same IoT devices100 may all support GPS location determination, may all connect to acellular network that supports a cellular device location service, andmay also all be able to connect to a WI-FI network that supports a WI-FIdevice location service. The user 150 may select a “GPS” tab inmeasurement accuracy region 810 to view or enter the measurementaccuracy for the GPS geo-location measurements of the locations of eachIoT device 100, and to view or enter the data source for the GPSmeasurements. For example, the GPS measurement accuracy may be 5 meters,and the data source may be the geo-location unit 325 of the IoT device100.

The user 150 may further select a “cellular” tab in measurement accuracyregion 810 to view or enter the measurement accuracy for the cellularnetwork triangulation geo-location measurements of the locations of eachIoT device 100, and to view or enter the data source for the cellularlocation measurements. For example, the cellular triangulationgeo-location measurements may be 100-150 meters, and the data source maybe the network address of wireless location determining function 125-2.

The user 105 may also select a “WI-FI” tab in measurement accuracyregion 810 to view or enter the measurement accuracy for the wirelessLAN location determining measurements of the locations of each IoTdevice 100, and to view or enter the data source for the cellularlocation measurements. For example, the WI-FI location for each IoTdevice 100 may be determined to be the geo-location of the current WI-FIhotspot to which the IoT device 100 is connected (e.g., a measurementaccuracy of possibly tens or hundreds of meters), and the data sourcemay the network address of the wireless location determining function125-1 in the LAN wireless network 130.

Spoof detection frequency region 815 presents the different detectionfrequencies that the user 150 may select for the group of IoT devices100 being configured. Therefore, entry region 815 enables the user 150to define how frequently the IoT device location data from at least twodifferent location sources are compared to one another to detectlocation spoofing. In the example region 815 shown in FIG. 8, acontinuous, periodic, on-request, or location change frequencies may beselected by the user 150. The continuous frequency indicates that thecurrent primary IoT device location is compared against the secondaryIoT device location whenever a new primary or secondary locationmeasurement is obtained. The periodic frequency indicates that thecurrent primary IoT device location, and the secondary IoT devicelocation, are compared at periodic intervals. The on-request frequencyindicates that the current primary and secondary location measurementsare compared only upon request (e.g., by the user 150). The “on locationchange of x meters” frequency indicates that the current primary andsecondary location measurements are compared when the IoT device 100 hasincurred a location change of a specified number of meters x. The numberof meters x may be selected by the user, or may be a default value.

Spoof detection notification frequency region 820 presents differentnotification frequencies that the user 150 may select for the group ofIoT devices 100 being configured. Therefore, entry region 820 enablesthe user 150 to define how many alarm notifications are generated pereach location spoofing detection. In the example region 820 shown inFIG. 8, a “once per detection,” a “periodic until acknowledged,” and a“once until device moves” notification frequencies may be selected. The“once per detection” option configures the location spoofing detectionservice to generate one alarm notification per each location spoofingdetection. The “periodic until acknowledged” option configures thelocation spoofing detection service to continue generating an alarmnotification on a specific periodic interval, until the user 150acknowledges the alarm. The “once until device moves” option configuresthe location spoofing detection service to generate a single alarmnotification as long as the IoT device 100 is stationary, but togenerate one or more subsequent alarm notifications as the IoT device100 moves to one or more additional locations.

Notification method region 825 presents different notification methodsthat the user 150 may select for the group of IoT devices 100 beingconfigured. Therefore, region 825 enables the user 150 to define themethod by which the user 150 is notified of a location spoofingdetection. As shown in the example region 825 of FIG. 8, the selectablenotification methods include email, text, streaming, API polling, orclient alarm. For email notification, the user 150 enters the emailaddress to which the location spoofing detection alarm notificationemail should be sent. For text notification, the user 150 enters thetextable phone number to which the location spoofing detection alarmnotification text should be sent. For streaming notification, the user150 enters the target device network address (e.g., Internet Protocol(IP) Address, or phone number) to which the location spoofing detectionalarm notification is streamed. For API polling notification, selectionof this notification option causes thingspace platform 210 to store anotification alarm which the user 150 may poll (e.g., via user device220) to retrieve the alarm notification. For client alarm notification,the user 150 may provide the network address (e.g., IP address ortelephone number) of the device in which is installed a target clientthat receives the alarm notification.

Location spoofing detection enhancements region 830 presents differentIoT device 100 or location spoofing detection dependent enhancementsthat may be available for the user 150 to select for location spoofingdetection of the group of IoT devices 100 being configured. Therefore,region 830 enables the user 150 to select enhancements that may beapplied to the location spoofing detection. In the example shown in FIG.8, the location spoofing detection enhancements may include mobilemovement enhancements, where an additional measurement accuracy error isapplied to IoT devices 100 that are nonstationary. For example, the user150 may select a “speed” mobile movement enhancement by which aninstantaneous speed of the IoT device is measured, and the value of themeasured speed is used in the location spoofing detection. As anotherexample, the user 150 may select a “handovers” mobile movementenhancement that tracks a number of handovers, within the servingcellular network 130-2, that occurs for the IoT device over time, anduses those tracked number of handovers in the location spoofingdetection. Upon completion of the location spoofing detectionconfiguration, the user 150 may submit the configuration data by, forexample, selecting a “submit” button 835 within user interface 800(e.g., touching the button upon the touch screen display). When theconfiguration data is submitted, the user device 220 sends theconfiguration data to thingspace platform 210 for storage in IoT deviceDB 230. Alternatively, instead of user entry via user interface 800, theconfiguration data for the IoT device, or group of IoT devices, may belisted in a file (e.g., in a .csv file format) that may be uploaded tothingspace platform 210. FIG. 9 depicts user device 220 supplyinglocation spoofing detection configuration data for the IoT device/groupof IoT devices to thingspace platform 210.

Thingspace platform 210 stores the location spoofing detectionconfiguration data for the device/device group in IoT device DB 230(block 630). The device group ID for the group of IoT devices 100 beingconfigured are stored in field 510, the location services supported datais stored in field 515, the measurement accuracy data is stored in field520, the data source data is stored in field 525, the spoofing detectionfrequency data is stored in field 530, the detection notificationfrequency data is stored in field 535, the notification method is storedin field 540, and the spoofing detection enhancements data are stored infield 545 of an entry 505 of location spoofing detection configurationdata structure 500 in IoT devices DB 230. FIG. 9 depicts thingspaceplatform 210 storing 915 the location spoofing detection configurationdata for the IoT device/group of IoT devices in IoT device DB 230.

Thingspace platform 210 initiates the provisioning and configuration ofa location spoofing detection service to the device/device group (block640). Thingspace platform 210 may, for example, initiate locationdetermination of the IoT devices 100 with wireless location determiningfunction 125-1 in wireless LAN network 130-1, and/or with wirelesslocation determining function 125-2 in cellular network 130-2. Uponinitiation of the location determination, wireless location determiningfunction 125-1 may report (e.g., periodically), to thingspace platform210, the current location of connected IoT devices 100 within WLAN(s)130-1, and/or wireless location determining function 125-2 may report(e.g., periodically), to thingspace platform 210, the current locationof connected IoT devices 100 within cellular network(s) 130-2. FIG. 9depicts thingspace platform 210 initiating 920 the provisioning andconfiguration of location spoofing detection service to the enrolled IoTdevice/group of IoT devices.

FIG. 10 is a flow diagram that illustrates an exemplary process forperforming location spoofing detection for an IoT device 100 based oncurrent primary and secondary locations associated with the IoT device100. The exemplary process of FIG. 10 may be implemented by thingspaceplatform 210, in conjunction with IoT device DB 230, and one or morewireless location determination functions 125. The exemplary process ofFIG. 10 is described with respect to the operations/messaging/signalingdiagram of FIG. 11.

The exemplary process includes thingspace platform 210 determining ifprimary location data associated with the IoT device 100 has beenreceived (block 1000). The primary location data may include GPScoordinates received from the IoT device 100 itself, wirelessnetwork-generated device location data received from wireless locationdetermining function 125-1 in wireless LAN network 130-1, or wirelessnetwork-generated device location data received from wireless locationdetermining function 125-2 in cellular network(s) 130-2. The primarylocation data source may have been selected by the user 150 duringlocation spoofing detection configuration (e.g., block 620 of FIG. 6).FIG. 11 depicts IoT device 100 sending a Cat-M1 report 1100 thatincludes a timestamp, and thingspace platform 210 receiving primary IoTdevice location data 1105 from a location source. In one implementation,the primary IoT device location data 1105 may include GPS coordinatedata, and may be received from the IoT device 100 within the Cat-M1report 1100. In another implementation, the primary location data mayinclude wireless network-generated device location data received fromwireless location determining function 125-2 in cellular network(s)130-2. If the IoT device 100's primary location data has not beenreceived (NO—block 1000), then block 1000 repeats until the primarylocation data is received by thingspace platform 210.

If the primary location data has been received (YES—block 1000), thenthingspace platform 210 determines if it is time, for the IoT device100, to perform location spoofing detection (block 1005). The user 150may specify a location spoofing detection frequency during locationspoofing detection configuration (e.g., block 620 of FIG. 6), andthingspace platform 210 may wait an amount of time prior to performingthe location spoofing detection. The wait time may be based on theconfiguration data supplied by the user 150 associated with the IoTdevice(s) 100. If it is not time to perform location spoofing detection(NO—block 1005), then the process continues with the repeat of block1000.

If it is time to perform location spoofing detection (YES—block 1005),then thingspace platform 210 determines if secondary location dataassociated with the IoT device 100 has been received (block 1010). Thesecondary location data may include, for example, wirelessnetwork-generated device location data received from wireless locationdetermining function 125-1 in WLAN network 130-1, or wirelessnetwork-generated device location data received from wireless locationdetermining function 125-2 in cellular network(s) 130-2. For example, ifthe primary location data includes GPS coordinates received from the IoTdevice 100 itself, then the secondary location data may include wirelessnetwork-generated device location data received from wireless locationdetermining function 125-1 in WLAN 130-1, or wireless network-generateddevice location data received from wireless location determiningfunction 125-2 in cellular network(s) 130-2. As another example, if theprimary location data includes wireless network-generated devicelocation data received from wireless location determining function 125-2in cellular network(s) 130-2, then the secondary location data mayinclude wireless network-generated device location data received fromwireless location determining function 125-1 in WLAN 130-1. Thesecondary location data source may have been selected by the user 150during location spoofing detection configuration (e.g., block 620 ofFIG. 6). FIG. 11 depicts thingspace platform 210 receiving secondary IoTdevice location data 1110.

If secondary location data has not been received for the IoT device(NO—block 1010), then thingspace platform 210 stores the primarylocation data in IoT device DB 230 (block 1015), and the processcontinues with a repeat of block 1000. If secondary location data hasbeen received for the IoT device 100 (YES—block 1010), thingspaceplatform 210 performs location spoofing detection using the currentprimary location data and the secondary location data for the IoT device100 (block 1020). In one implementation, location spoofing is detectedby spoofing detection function 120 of thingspace platform 210 when thesum of the absolute value of the distance between the primary locationand the secondary location is greater than the total measurementaccuracy (location spoofing detection=absolute value of the distancebetween primary location and secondary location>total measurementaccuracy). The total measurement accuracy, as referred to herein, equalsthe sum of the measurement accuracy associated with the primary locationdata and the measurement accuracy associated with the secondary locationdata.

In another implementation, if the user 150 has selected locationspoofing detection enhancements during location spoofing detectionconfiguration (e.g., during block 620 of FIG. 6), then location spoofingmay be detected by spoofing detection function 120 of thingspaceplatform 210 when the sum of the absolute value of the distance betweenthe primary location and the secondary location plus a mobility movementadjustment is greater than the total measurement accuracy (locationspoofing detection=(absolute value of distance between primary locationand secondary location+mobility movement adjustment)>total measurementaccuracy. The mobility movement adjustment is described further below.

If, during location spoofing detection configuration, the user 150 hasselected a “speed” location spoofing detection enhancement, then themobility movement adjustment may be set equal to the IoT device 100'sspeed multiplied by the reporting delay (speed*reporting delay), wherethe reporting delay is the interval between the time at which the IoTdevice 100's speed was determined, and the time at which spoofingdetection function 120 detects location spoofing of the IoT device 100.For example, a stationary IoT device (e.g., a water meter) would have aspeed of zero, equaling a mobility adjustment of zero. As anotherexample, if an IoT device 100 that is capable of reporting theinstantaneous speed of the device 100, reports a speed of 14meters/second and the reporting delay is 5 seconds, then the mobilitymovement adjustment would be 14 m/s*5 s=70 meters.

If, during location spoofing detection configuration, the user 150 hasselected a “handovers” location spoofing detection enhancement, then themobility movement adjustment may be set equal to the number of handoversmultiplied by the average cell distance divided by the sampling timemultiplied by the reporting delay (#handovers*(average celldistance/sampling time)*reporting delay). For example, if an IoT device100 that is capable of collecting and reporting a number of handoversbetween cells reports three handovers over a sampling time of 10seconds, with an average cell distance of 75 meters and a reportingdelay of 5 seconds, then the mobility movement adjustment would be 3*(75m/10 s)*5 s=112.5 m. Other types of handover adjustment algorithms mayalternatively be used, including algorithms that take into accounthierarchical cell planning. FIG. 11 depicts thingspace platform 210performing 1115 location spoofing detection using the received IoTdevice primary location data and IoT device secondary location data.

If thingspace platform 210 has detected location spoofing in block 1020(YES—block 1025), then thingspace platform 210 generates a locationspoofing detection alarm notification for the IoT device 100 (block1030). The location spoofing detection alarm notification may begenerated, and supplied to the user 150 associated with the IoT device100 based on the location spoofing detection configuration data of block620 of FIG. 6. Thus, the location spoofing detection alarm notificationmay be supplied at a specified frequency (e.g., once per detection,periodic until acknowledged, once until device moves), and via aspecified method (e.g., email, text, streaming, polling, client alarm).The location spoofing detection alarm notification may include data thatidentifies the IoT device 100, and which may include the last knownvalid location of the IoT device 100 prior to the detection of locationspoofing. FIG. 11 depicts spoofing detection function 120 of thingspaceplatform 210 detecting 1120 location spoofing, and sending a locationspoofing detection alarm notification 1125 to user device 220. Ifthingspace platform 210 has not detected location spoofing (NO—block1025), then the process continues with a repeat of block 1000. Theexemplary process of FIG. 10 may be repeated by thingspace platform 210continuously, or periodically (e.g., every 0.5 seconds).

FIG. 12 is a flow diagram that illustrates an exemplary process fordetermining a confidence level associated with a location of an IoTdevice 100 based on current primary and secondary locations associatedwith the IoT device 100. The exemplary process of FIG. 12 may beimplemented by thingspace platform 210, in conjunction with IoT deviceDB 230, and one or more wireless location determination functions 125.The exemplary process of FIG. 12 is described with respect to theoperations/messaging diagram of FIG. 13.

The exemplary process includes thingspace platform 210 determining ifprimary location data associated with the IoT device 100 has beenreceived (block 1200). The primary location data may include GPScoordinates received from the IoT device 100 itself, wirelessnetwork-generated device location data received from wireless locationdetermining function 125-1 in wireless LAN network 130-1, or wirelessnetwork-generated device location data received from wireless locationdetermining function 125-2 in cellular network(s) 130-2. The primarylocation data source may have been selected by the user 150 duringlocation spoofing detection configuration (e.g., block 620 of FIG. 6).FIG. 13 depicts IoT device 100 sending a Cat-M1 report 1300 thatincludes a timestamp, and thingspace platform 210 receiving primary IoTdevice location data 1305 from a location source. In one implementation,the primary IoT device location data 1305 may include GPS coordinatedata, and may be received from the IoT device 100 within the Cat-M1report 1300. If the IoT device 100's primary location data has not beenreceived (NO—block 1200), then block 1200 repeats until the primarylocation data is received by thingspace platform 210.

If the primary location data has been received (YES—block 1200), thenthingspace platform 210 determines if it is time, for thingspaceplatform 210, to determine a location confidence level associated withthe geo-location of the IoT device 100 (block 1205). The user 150 mayspecify a location confidence level determination frequency during theconfiguration process (e.g., block 620 of FIG. 6), and thingspaceplatform 210 may wait an amount of time determined by the user-suppliedconfiguration data prior to performing the location confidence leveldetermination. If it is not time to determine a location confidencelevel associated with the IoT device 100 (NO—block 1205), then theprocess continues with the repeat of block 1200.

If it is time to determine a location confidence level associated withthe IoT device 100 (YES—block 1205), then thingspace platform 210determines if secondary location data associated with the IoT device 100has been received (block 1210). The secondary location data may includewireless network-generated device location data received from wirelesslocation determining function 125-1 in WLAN network 130-1, or wirelessnetwork-generated device location data received from wireless locationdetermining function 125-2 in cellular network(s) 130-2. For example, ifthe primary location data includes GPS coordinates received from the IoTdevice 100 itself, then the secondary location data may include wirelessnetwork-generated device location data received from wireless locationdetermining function 125-1 in WLAN network 130-1, or wirelessnetwork-generated device location data received from wireless locationdetermining function 125-2 in cellular network(s) 130-2. As anotherexample, if the primary location data includes wirelessnetwork-generated device location data received from wireless locationdetermining function 125-2 in cellular network(s) 130-2, then thesecondary location data may include wireless network-generated devicelocation data received from wireless location determining function 125-1in WLAN network 130-1. The secondary location data source may have beenselected by the user 150 during location spoofing detectionconfiguration (e.g., block 620 of FIG. 6). FIG. 13 depicts thingspaceplatform 210 receiving secondary IoT device location data 1310.

If secondary location data has not been received for the IoT device(NO—block 1210), then thingspace platform 210 stores the primarylocation data in IoT device DB 230 (block 1215), and the processcontinues with a repeat of block 1200. If secondary location data hasbeen received for the IoT device 100 (YES—block 1210), thingspaceplatform 210 determines a location confidence level associated with theIoT device 100 using the IoT device's primary location data and thesecondary location data (block 1220). The location confidence level isan indicator of the likelihood that the IoT device 100 is actuallylocated at the location indicated by the primary location data. In oneimplementation, IoT device location confidence levels may be determinedby applying specified confidence thresholds to the primary and secondarylocation data. In an example wherein the IoT device location confidencelevels include “no confidence,” “low confidence,” and “high confidence,”the IoT device location confidence levels may be determined based on thefollowing:

No Confidence: The sum of the absolute value of the distance between theprimary location and the secondary location is greater than or equal tothe total measurement accuracy (No Confidence=absolute distance betweenprimary location and secondary location>total measurement accuracy).

Low Confidence: The sum of the absolute value of the distance betweenthe primary location and the secondary location is less than the totalmeasurement accuracy, but greater than or equal to the total measurementaccuracy minus a first specified confidence threshold TH₁ value (LowConfidence=absolute value of the distance between primary location dataand secondary location data<total measurement accuracy, but> or =totalmeasurement accuracy−TH₁)

High Confidence: The sum of the absolute distance between the primarylocation and the secondary location is less than the total measurementaccuracy minus TH₁, but greater than the total measurement accuracyminus a second specified confidence threshold TH₂ value (HighConfidence=absolute value of the distance between primary location andsecondary location<total measurement accuracy−TH₁, but> or =totalmeasurement accuracy−TH₂, where TH₂>TH₁).

The “no confidence” confidence level indicates that it is very likelythat the IoT device is not at the location indicated by the primarylocation data. The “low confidence” confidence level indicates that itis not likely that the IoT device 100 is at the location indicated bythe primary location data. The “high confidence” confidence levelindicates that it is very likely that the IoT device 100 is at thelocation indicated by the primary location data.

In another implementation, if the user 150 has selected locationspoofing detection enhancements during configuration (e.g., during block620 of FIG. 6), then the IoT device location confidence levels may bealternatively determined based on the following:

No Confidence: The sum of the absolute value of the distance between theprimary location and the secondary location plus the mobility movementadjustment is greater than or equal to the total measurement accuracy(No Confidence=absolute value of the distance between primary locationand secondary location+mobility movement adjustment>total measurementaccuracy). Examples of mobility movement adjustments are describedfurther below.

Low Confidence: The sum of the absolute value of the distance betweenthe primary location and the secondary location plus the mobilitymovement adjustment is less than the total measurement accuracy, butgreater than or equal to the total measurement accuracy minus a firstspecified confidence threshold TH₁ value (Low Confidence=absolute valueof the distance between primary location and secondary location+mobilitymovement adjustment<total measurement accuracy, but> or =totalmeasurement accuracy−TH₁)

High Confidence: The sum of the absolute value of the distance betweenthe primary location and the secondary location plus the mobilitymovement adjustment is less than the total measurement accuracy minusTH₁, but greater than the total measurement accuracy minus a secondspecified confidence threshold TH₂ value (High Confidence=absolute valueof the distance between primary location and secondary location<totalmeasurement accuracy−TH₁, but> or =total measurement accuracy−TH₂, whereTH₂>TH₁).

If, during location spoofing detection configuration, the user 150 hasselected a “speed” location spoofing detection enhancement, then themobility movement adjustment may be set equal to the IoT device 100'sspeed multiplied by the reporting delay (speed*reporting delay), wherethe reporting delay is the interval between the time at which the IoTdevice 100's speed was determined, and the time at which spoofingdetection function 120 detects location spoofing of the IoT device 100.For example, a stationary IoT device (e.g., a water meter) would have aspeed of zero, resulting in a mobility movement adjustment of zero. Asanother example, if an IoT device 100 that is capable of measuring theinstantaneous speed of the device 100, reports a speed of 14meters/second and the reporting delay is 5 seconds, then the mobilitymovement adjustment would be 14 m/s*5 s=70 meters.

If, during location spoofing detection configuration, the user 150 hasselected a “handovers” location spoofing detection enhancement, then themobility movement adjustment may be set equal to the number of handoversmultiplied by the average cell distance divided by the sampling timemultiplied by the reporting delay (#handovers*(average celldistance/sampling time)*reporting delay). As an illustrative example, ifan IoT device 100 that is capable of collecting and detecting a numberof handovers between cells, reports three handovers over a sampling timeof 10 seconds, with an average cell distance of 75 meters and areporting delay of 5 seconds, then the mobility movement adjustmentwould be 3*(75 m/10 s)*5 s=112.5 m. Other types of handover adjustmentalgorithms may alternatively be used, including algorithms that takeinto account hierarchical cell planning. FIG. 13 depicts device locationconfidence determination function 155 of thingspace platform 210determining 1315 the location confidence level associated with an IoTdevice 100 using primary and secondary location data received for thatIoT device 100.

Thingspace platform 210 generates a notification for the IoT device 100that indicates the IoT device location confidence level determined inblock 1220 (block 1225). The notification may be generated, and suppliedto the user 150 associated with the IoT device 100 based on theconfiguration data of block 620 of FIG. 6. Thus, the notification may besupplied at a frequency, and via a method (e.g., email, text, streaming,polling, client alarm) specified by the user 150 in the configurationdata. The location spoofing detection alarm notification may includedata that identifies the IoT device 100, the IoT device locationconfidence level, and the last known location of the IoT device 100 atwhich the IoT device location confidence level was a high confidencelevel. FIG. 13 depicts device location confidence determination function155 of thingspace platform 210 providing the determined IoT devicelocation confidence level 1320 to a user device 220 associated with theuser 150 of the IoT device 100. The exemplary process of FIG. 12 may berepeated by thingspace platform 210 continuously, or periodically (e.g.,every 0.5 seconds).

The foregoing description of implementations provides illustration anddescription, but is not intended to be exhaustive or to limit theinvention to the precise form disclosed. Modifications and variationsare possible in light of the above teachings or may be acquired frompractice of the invention. For example, while series of blocks have beendescribed with respect to FIGS. 6, 10, and 12, andoperations/signaling/message flows with respect to FIGS. 9, 11, and 13,the order of the blocks and/or operations/signaling/message flows may bevaried in other implementations. Moreover, non-dependent blocks may beperformed in parallel.

To the extent the aforementioned embodiments collect, store, or employpersonal information of individuals, it should be understood that suchinformation shall be collected, stored, and used in accordance with allapplicable laws concerning protection of personal information.Additionally, the collection, storage, and use of such information canbe subject to consent of the individual to such activity, for example,through well known “opt-in” or “opt-out” processes as can be appropriatefor the situation and type of information. Storage and use of personalinformation can be in an appropriately secure manner reflective of thetype of information, for example, through various encryption andanonymization techniques for particularly sensitive information.

Certain features described above may be implemented as “logic” or a“unit” that performs one or more functions. This logic or unit mayinclude hardware, such as one or more processors, microprocessors,application specific integrated circuits, or field programmable gatearrays, software, or a combination of hardware and software.

No element, act, or instruction used in the description of the presentapplication should be construed as critical or essential to theinvention unless explicitly described as such. Also, as used herein, thearticle “a” is intended to include one or more items. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

In the preceding specification, various preferred embodiments have beendescribed with reference to the accompanying drawings. It will, however,be evident that various modifications and changes may be made thereto,and additional embodiments may be implemented, without departing fromthe broader scope of the invention as set forth in the claims thatfollow. The specification and drawings are accordingly to be regarded inan illustrative rather than restrictive sense.

What is claimed is:
 1. A network device, comprising: a communication interface connected to a network and configured to: receive primary location data, associated with a device connected to an Internet of Things (IoT) device service, wherein the primary location data is generated by a first location determining source that is located in the device, and wherein the primary location data is associated with a primary location, and receive secondary location data, associated with the device, generated by a second location determining source that is located external to the device, wherein the secondary location data is associated with a secondary location; and a processing unit connected to the communication interface and configured to: perform location spoofing detection of the device based on the primary location data and the secondary location data, wherein, when performing the location spoofing detection, the processing unit is further configured to: detect when a value of a distance between the primary location and the secondary location is greater than a total measurement accuracy, wherein the total measurement accuracy equals a sum of a first measurement accuracy associated with the primary location data and a second measurement accuracy associated with the secondary location data.
 2. The network device of claim 1, wherein the device is an IoT user equipment (UE) device comprising a battery powered IoT device or a Machine-to-Machine (M2M) device.
 3. The network device of claim 1, wherein the first location determining source comprises a Global Positioning System (GPS) device, and the second location determining source comprises a cellular network location determining function or a wireless Local Area Network (LAN) location determining function.
 4. The network device of claim 1, wherein the IoT device service comprises a low power wide area cellular network service.
 5. The network device of claim 1, wherein the communication interface is further configured to: receive configuration data for configuring the IoT device service for one or more devices that include the device, wherein the configuration data identifies the first location determining source and the second location determining source.
 6. The network device of claim 5, wherein the configuration data includes a notification method and wherein the processing unit is further configured to: generate a location spoofing detection alarm notification in accordance with the notification method.
 7. The network device of claim 1, wherein, when performing location spoofing detection of the device, the processing unit is further configured to: determine one of a plurality of confidence levels associated with the device being located at the primary location based on one or more confidence thresholds, the primary location data, and the secondary location data.
 8. The network device of claim 7, wherein, when determining one of the plurality of confidence levels associated with the device being located at the primary location, the processing unit is further configured to: determine one of a no confidence, low confidence, or high confidence level that indicates a degree of likelihood that the device is located at the primary location.
 9. A method, comprising: connecting a device to an Internet of Things (IoT) device service; receiving, by a network device, primary location data associated with the device connected to the IoT device service, wherein the primary location data is generated by a first location determining source that is located in the device, and wherein the primary location data is associated with a primary location; receiving, by the network device, secondary location data associated with the device that is generated by a second location determining source that is located external to the device, wherein the secondary location data is associated with a secondary location; and performing location spoofing detection of the device based on the primary location data and the secondary location data, wherein performing the location spoofing detection further comprises: detecting when a value of a distance between the primary location and the secondary location is greater than a total measurement accuracy, wherein the total measurement accuracy equals a sum of a first measurement accuracy associated with the primary location data and a second measurement accuracy associated with the secondary location data.
 10. The method of claim 9, wherein the device is an IoT user equipment (UE) device comprising a battery powered IoT device or a Machine-to-Machine (M2M) device.
 11. The method of claim 9, wherein the first location determining source comprises a Global Positioning System (GPS) device, and the second location determining source comprises a cellular network location determining function or a wireless Local Area Network (LAN) location determining function.
 12. The method of claim 9, further comprising: receiving configuration data for configuring the IoT device service for one or more devices that include the device, wherein the configuration data identifies the first location determining source and the second location determining source.
 13. The method of claim 12, wherein the configuration data includes a notification method and wherein the method further comprises: generating a location spoofing detection alarm notification in accordance with the notification method.
 14. The method of claim 9, wherein performing location spoofing detection of the device further comprises: determining one of a plurality of confidence levels associated with the device being located at the primary location based on one or more confidence thresholds, the primary location data, and the secondary location data.
 15. The method of claim 14, wherein determining one of the plurality of confidence levels further comprises: determining one of a no confidence, low confidence, or high confidence level that indicates a degree of likelihood that the device is located at the primary location.
 16. A system, comprising: a communication interface connected to a network and configured to: receive primary location data, associated with an Internet of Things (IoT) device, generated by a first location determining source that is located in the IoT device, wherein the primary location data is associated with a primary location, and receive secondary location data, associated with the IoT device, generated by a second location determining source that is located external to the IoT device, wherein the secondary location data is associated with a secondary location; and a processor or logic connected to the communication interface and configured to: perform location spoofing detection of the IoT device based on the primary location data and the secondary location data, wherein, when performing the location spoofing detection, the processor or logic is further configured to: detect when a value of a distance between the primary location and the secondary location is greater than a total measurement accuracy, wherein the total measurement accuracy equals a sum of a first measurement accuracy associated with the primary location data and a second measurement accuracy associated with the secondary location data; and generate an alarm notification based on the performance of the location spoofing detection.
 17. The system of claim 16, wherein the first location determining source comprises a Global Positioning System (GPS) device, and the second location determining source comprises a cellular network location determining function or a wireless Local Area Network (LAN) location determining function.
 18. The system of claim 16, wherein, when performing location spoofing detection of the device, the processor or logic is further configured to: determine one of a plurality of confidence levels associated with the IoT device being located at the primary location based on one or more confidence thresholds, the primary location data, and the secondary location data.
 19. The system of claim 18, wherein, when determining one of the plurality of confidence levels associated with the IoT device being located at the primary location, the processor or logic is further configured to: determine one of a no confidence, low confidence, or high confidence level that indicates a degree of likelihood that the IoT device is located at the primary location.
 20. The system of claim 16, wherein the IoT device is connected to an IoT device service, and wherein the communication interface is further configured to: receive configuration data for configuring the IoT device service for one or more devices that include the IoT device, wherein the configuration data identifies the first location determining source and the second location determining source. 